Identification of dominant features in spatial data
نویسندگان
چکیده
Dominant features of spatial data are connected structures or patterns that emerge from location-based variation and manifest at specific scales resolutions. To identify dominant features, we propose a sequential application multiresolution decomposition variogram function estimation. Multiresolution separates into additive components, in this way enables the recognition their features. A dedicated method is developed for arbitrary gridded data, where underlying model includes precision spatial-weight matrix to capture correlation. The separated components by smoothing on different scales, such larger have longer correlation ranges. Moreover, our can handle missing values, which often useful applications. Variogram estimation be used describe properties data. Such functions therefore estimated each component determine its effective range, assesses width-extent feature. Finally, Bayesian analysis inference identified judge whether these credibly different. efficient implementation relies mainly sparse-matrix structure algorithms. By applying simulated demonstrate applicability theoretical soundness. In disciplines use lead new insights, as exemplify identifying forest dataset. application, width-extents an ecological interpretation, namely species interaction estimates support derivation ecosystem biodiversity indices.
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ژورنال
عنوان ژورنال: spatial statistics
سال: 2021
ISSN: ['2211-6753']
DOI: https://doi.org/10.1016/j.spasta.2020.100483